Page 92 - Read Online
P. 92

Page 8 of 9                                                       Wang et al. J. Mater. Inf. 2026, 6, 1




               Copyright
               © The Author(s) 2026.

               REFERENCES
               1.  Merchant, A.; Batzner, S.; Schoenholz, S. S.; Aykol, M.; Cheon, G.; Cubuk, E. D. Scaling deep learning for materials discovery. Nature
                  2023, 624, 80-5. DOI PubMed PMC
               2.  Zeni, C.; Pinsler, R.; Zügner, D.; et al. A generative model for inorganic materials design. Nature 2025, 639, 624-32. DOI PubMed PMC
               3.  Xu, P.; Ma, Y.; Lu, W.; Li, M.; Zhao, W.; Dai, Z. Multi-objective optimization in machine learning assisted materials design and
                  discovery. J. Mater. Inf. 2025, 5, 26. DOI
               4.  Westermayr, J.; Gilkes, J.; Barrett, R.; Maurer, R. J. High-throughput property-driven generative design of functional organic molecules.
                  Nat. Comput. Sci. 2023, 3, 139-48. DOI PubMed
               5.  Biyela, S.; Dihal, K.; Gero, K. I.; et al. Generative AI and science communication in the physical sciences. Nat. Rev. Phys. 2024, 6,
                  162-5. DOI
               6.  Njirjak, M.; Žužić, L.; Babić, M.; et al. Reshaping the discovery of self-assembling peptides with generative AI guided by hybrid deep
                  learning. Nat. Mach. Intell. 2024, 6, 1487-500. DOI
               7.  Li, Z.; Yang, M.; Park, J.; Wei, S.; Berry, J. J.; Zhu, K. Stabilizing perovskite structures by tuning tolerance factor: formation of
                  formamidinium and cesium lead iodide solid-state alloys. Chem. Mater. 2016, 28, 284-92. DOI
               8.  Liu, X.; Luo, D.; Lu, Z. H.; et al. Stabilization of photoactive phases for perovskite photovoltaics. Nat. Rev. Chem. 2023, 7, 462-79. DOI
                  PubMed
               9.  Song, Z.; Liu, Q. Tolerance factor and phase stability of the normal spinel structure. Cryst. Growth. Des. 2020, 20, 2014-8. DOI
               10.  Song, Z.; Zhou, D.; Liu, Q. Tolerance factor and phase stability of the garnet structure. Acta. Crystallogr. C. Struct. Chem. 2019, 75,
                  1353-8. DOI PubMed
               11.  Wang, Z.; Lin, X.; Han, Y.; et al. Harnessing artificial intelligence to holistic design and identification for solid electrolytes. Nano.
                  Energy. 2021, 89, 106337. DOI
               12.  Zhu, T.; Huhn, W. P.; Wessler, G. C.; et al. I 2–II–IV–VI 4 (I = Cu, Ag; II = Sr, Ba; IV = Ge, Sn; VI = S, Se): chalcogenides for thin-film
                  photovoltaics. Chem. Mater. 2017, 29, 7868-79. DOI
               13.  Wang, Z.; Cai, J.; Wang, Q.; Wu, S.; Li, J. Unsupervised discovery of thin-film photovoltaic materials from unlabeled data. npj. Comput.
                  Mater. 2021, 7, 596. DOI
               14.  Curtarolo, S.; Hart, G. L.; Nardelli, M. B.; Mingo, N.; Sanvito, S.; Levy, O. The high-throughput highway to computational materials
                  design. Nat. Mater. 2013, 12, 191-201. DOI PubMed
               15.  Griesemer, S. D.; Xia, Y.; Wolverton, C. Accelerating the prediction of stable materials with machine learning. Nat. Comput. Sci. 2023,
                  3, 934-45. DOI PubMed
               16.  Wu, Z.; Zhang, O.; Wang, X.; et al. Leveraging language model for advanced multiproperty molecular optimization via prompt
                  engineering. Nat. Mach. Intell. 2024, 6, 1359-69. DOI
               17.  Jiang, X.; Wang, W.; Tian, S.; Wang, H.; Lookman, T.; Su, Y. Applications of natural language processing and large language models in
                  materials discovery. npj. Comput. Mater. 2025, 11, 1554. DOI
               18.  Bartel, C. J.; Sutton, C.; Goldsmith, B. R.; et al. New tolerance factor to predict the stability of perovskite oxides and halides. Sci. Adv.
                  2019, 5, eaav0693. DOI PubMed PMC
               19.  Bassen, G.; Wilfong, B.; Bunstine, W.; Edmiston, N.; Siegler, M. A.; McQueen, T. M. Tolerance factor approach for the design of
                  quaternary materials as applied to the A 2Ln 4Cu 2nQ 7+n homologous series. J. Am. Chem. Soc. 2024, 146, 25190-9. DOI PubMed
               20.  Molokeev, M. S.; Kuznetsov, S. O. Tolerance factor for huntite-family compounds. Phys. Solid. State. 2020, 62, 2058-62. DOI
               21.  Mouta, R.; Silva, R. X.; Paschoal, C. W. Tolerance factor for pyrochlores and related structures. Acta. Crystallogr. B. Struct. Sci. Cryst.
                  Eng. Mater. 2013, 69, 439-45. DOI PubMed
               22.  Smith, M.; Li, Z.; Landry, L.; Merz, K. M. Jr.; Li, P. Consequences of overfitting the van der Waals radii of ions. J. Chem. Theory.
                  Comput. 2023, 19, 2064-74. DOI PubMed
               23.  Marchenko, E. I.; Fateev, S. A.; Eremin, N. N.; Chen, Q.; Goodilin, E. A.; Tarasov, A. B. Crystal chemical insights on lead iodide
                  perovskites doping from revised effective radii of metal ions. ACS. Materials. Lett. 2021, 3, 1377-84. DOI
               24.  Turnley, J. W.; Agarwal, S.; Agrawal, R. Rethinking tolerance factor analysis for chalcogenide perovskites. Mater. Horiz. 2024, 11,
                  4802-8. DOI PubMed
               25.  Mondal, D.; Mahadevan, P. Structural distortions in hybrid perovskites revisited. Chem. Mater. 2024, 36, 4254-61. DOI
               26.  Antoniuk, E. R.; Cheon, G.; Wang, G.; Bernstein, D.; Cai, W.; Reed, E. J. Predicting the synthesizability of crystalline inorganic
                  materials from the data of known material compositions. npj. Comput. Mater. 2023, 9, 1114. DOI
   87   88   89   90   91   92   93   94   95   96   97